Regionalization of post-processed ensemble runoff forecasts

For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather), and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather). The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). Originally, the post-processing parameters were identified as a fixed set of parameters for a region. Later there were methods for regionalizing the post-processed output, but still with regionally constant parameters (Berrocal et al., 2007). In hydrology, Hemri et al. (2013) extended the method to have temporally consistent parameters between time steps for a single location. Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. The application of our work is the European Flood Awareness System (http://www.efas.eu), where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than Engeland and Steinsland (2014) which is likely to make their method unfeasible in practice. We also want to regionalize the parameters themselves for other locations than the calibration gauges. Lastly, not all forecasts are available for all lead times, as in their approach. We are therefore testing a slightly different approach, where the post-processing parameters are estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014). We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.

SKOIEN Jon;  BOGNER Konrad;  SALAMON Peter;  SMITH Paul;  PAPPENBERGER Florian; 

2016-09-16

Copernicus

JRC100584

2199-8981,   

http://www.proc-iahs.net/373/109/2016/,    https://publications.jrc.ec.europa.eu/repository/handle/JRC100584,   

10.5194/piahs-373-109-2016,